The role of facilities management (FM) is evolving, with building owners increasingly expecting FM teams to be drivers for business growth. The focus is shifting to highly efficient operations and an enhanced user experience, with teams expected to extend their roles as providers of transformative solutions and smart technology, placing greater pressure on them than ever before.

Today, the built environment requires systems to operate large portfolios of buildings at the highest efficiency level. The surge of IoT and machine learning are at the heart of the technology that will enable efficiencies of scale in FM, not to mention across numerous other industries such as healthcare, marketing and travel. Machine learning magnifies the value of data by removing manual work and replacing it with data-driven intelligence.

Artificial Intelligence (AI) and automation in FM not only optimises production, it also frees up valuable time and resources so that facilities managers can more meticulously oversee and recall details affecting day-to-day operations, which artificially intelligent systems do not have the capacity to deliver. In other words, they can focus more on serving customers and enhancing experiences. For example, self-vacuuming and self-mopping devices use AI to map out a floor, clean the floor, and return to a docking station, whilst facilities managers can take care of more human tasks.

AI will be an integral component of all FM activities soon

AI is being applied to all aspects of FM, from corporate real estate (CRE) providers, residential property managers, restaurants, as well as retailers. If something goes wrong or breaks down, users want immediate answers for rapid solutions. Fortunately, AI tools can identify these problems as soon as they occur, but the real value of AI in FM is seen from analysing historic and real-time data to identify correlations between existing performance and potential malfunctions, pre-empting the need for repair or replacement before a malfunction occurs.

Case studies of AI in FM

With AI and predictive analytics penetrating the FM industry, the biggest profit will be from using data insights that bring together operations, maintenance and sustainability for smart operations, everyday sustainability performance intelligence, alongside occupant engagement and productivity.

Drones

Using drones in FM can be extremely beneficial, not only for productivity and efficiency, but also in terms of safety. First of all, drones have the rare ability to operate in the majority of conditions, regardless of the weather or the presence of dangerous substances. Drones can also be used to access or monitor hard-to-reach areas where it might be dangerous to send human labourers. They can efficiently collect data, complete safety inspections, or capture project progress without any safety or conditional concerns.

Warehouse Management

AI and machine learning can test hundreds of demand forecasting models and possibilities with a new level of precision, while automatically adjusting to different variables such as new product introductions, supply chain disruptions or sudden changes in demand. Using AI systems, every single part can be tracked from when it’s first manufactured, to when it’s assembled and shipped to an end customer.

Example: Walmart cut taking physical inventory from one month to 24 hours by using sophisticated drones that fly through the warehouse, scan products, and check for misplaced items. Using algorithms that learn from experience to optimise logistics, BMW follows each part, from the point it was manufactured, to when the vehicle is sold—from all of its 31 assembly facilities located in over 15 countries.[1]

Making better products with generative design

AI is also changing the way we design products. These days, AI can actually create products using a detailed brief inputted by designers and engineers. One of the major advantages of this approach is that an AI algorithm is completely objective – it doesn’t default to what a human designer would regard as a “logical” starting point. No assumptions are taken at face value and everything is tested according to actual performance against a wide range of manufacturing scenarios and conditions.[2]